{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ResnetTrick_s192bs64\n",
    "\n",
    "> size 192 bs64 result 8744"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# setup and imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pip install git+https://github.com/ayasyrev/model_constructor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pip install git+https://github.com/kornia/kornia"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from kornia.contrib import MaxBlurPool2d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from fastai.basic_train import *\n",
    "from fastai.vision import *\n",
    "from fastai.script import *\n",
    "from model_constructor.net import *\n",
    "from model_constructor.layers import SimpleSelfAttention, ConvLayer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Ranger deep learning optimizer - RAdam + Lookahead combined.\n",
    "  #https://github.com/lessw2020/Ranger-Deep-Learning-Optimizer\n",
    "\n",
    "  #Ranger has now been used to capture 12 records on the FastAI leaderboard.\n",
    "\n",
    "  #This version = 9.3.19  \n",
    "\n",
    "  #Credits:\n",
    "  #RAdam -->  https://github.com/LiyuanLucasLiu/RAdam\n",
    "  #Lookahead --> rewritten by lessw2020, but big thanks to Github @LonePatient and @RWightman for ideas from their code.\n",
    "  #Lookahead paper --> MZhang,G Hinton  https://arxiv.org/abs/1907.08610\n",
    "\n",
    "  #summary of changes: \n",
    "  #full code integration with all updates at param level instead of group, moves slow weights into state dict (from generic weights), \n",
    "  #supports group learning rates (thanks @SHolderbach), fixes sporadic load from saved model issues.\n",
    "  #changes 8/31/19 - fix references to *self*.N_sma_threshold; \n",
    "                  #changed eps to 1e-5 as better default than 1e-8.\n",
    "\n",
    "import math\n",
    "import torch\n",
    "from torch.optim.optimizer import Optimizer, required\n",
    "import itertools as it"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# utils"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Mish(nn.Module):\n",
    "    def __init__(self):\n",
    "        super().__init__()\n",
    "        print(\"Mish activation loaded...\")\n",
    "\n",
    "    def forward(self, x):  \n",
    "        #save 1 second per epoch with no x= x*() and then return x...just inline it.\n",
    "        return x *( torch.tanh(F.softplus(x))) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "class Ranger(Optimizer):\n",
    "\n",
    "    def __init__(self, params, lr=1e-3, alpha=0.5, k=6, N_sma_threshhold=5, betas=(.95,0.999), eps=1e-5, weight_decay=0):\n",
    "        #parameter checks\n",
    "        if not 0.0 <= alpha <= 1.0:\n",
    "            raise ValueError(f'Invalid slow update rate: {alpha}')\n",
    "        if not 1 <= k:\n",
    "            raise ValueError(f'Invalid lookahead steps: {k}')\n",
    "        if not lr > 0:\n",
    "            raise ValueError(f'Invalid Learning Rate: {lr}')\n",
    "        if not eps > 0:\n",
    "            raise ValueError(f'Invalid eps: {eps}')\n",
    "\n",
    "        #parameter comments:\n",
    "        # beta1 (momentum) of .95 seems to work better than .90...\n",
    "        #N_sma_threshold of 5 seems better in testing than 4.\n",
    "        #In both cases, worth testing on your dataset (.90 vs .95, 4 vs 5) to make sure which works best for you.\n",
    "\n",
    "        #prep defaults and init torch.optim base\n",
    "        defaults = dict(lr=lr, alpha=alpha, k=k, step_counter=0, betas=betas, N_sma_threshhold=N_sma_threshhold, eps=eps, weight_decay=weight_decay)\n",
    "        super().__init__(params,defaults)\n",
    "\n",
    "        #adjustable threshold\n",
    "        self.N_sma_threshhold = N_sma_threshhold\n",
    "\n",
    "        #now we can get to work...\n",
    "        #removed as we now use step from RAdam...no need for duplicate step counting\n",
    "        #for group in self.param_groups:\n",
    "        #    group[\"step_counter\"] = 0\n",
    "            #print(\"group step counter init\")\n",
    "\n",
    "        #look ahead params\n",
    "        self.alpha = alpha\n",
    "        self.k = k \n",
    "\n",
    "        #radam buffer for state\n",
    "        self.radam_buffer = [[None,None,None] for ind in range(10)]\n",
    "\n",
    "        #self.first_run_check=0\n",
    "\n",
    "        #lookahead weights\n",
    "        #9/2/19 - lookahead param tensors have been moved to state storage.  \n",
    "        #This should resolve issues with load/save where weights were left in GPU memory from first load, slowing down future runs.\n",
    "\n",
    "        #self.slow_weights = [[p.clone().detach() for p in group['params']]\n",
    "        #                     for group in self.param_groups]\n",
    "\n",
    "        #don't use grad for lookahead weights\n",
    "        #for w in it.chain(*self.slow_weights):\n",
    "        #    w.requires_grad = False\n",
    "\n",
    "    def __setstate__(self, state):\n",
    "        print(\"set state called\")\n",
    "        super(Ranger, self).__setstate__(state)\n",
    "\n",
    "\n",
    "    def step(self, closure=None):\n",
    "        loss = None\n",
    "        #note - below is commented out b/c I have other work that passes back the loss as a float, and thus not a callable closure.  \n",
    "        #Uncomment if you need to use the actual closure...\n",
    "\n",
    "        #if closure is not None:\n",
    "            #loss = closure()\n",
    "\n",
    "        #Evaluate averages and grad, update param tensors\n",
    "        for group in self.param_groups:\n",
    "\n",
    "            for p in group['params']:\n",
    "                if p.grad is None:\n",
    "                    continue\n",
    "                grad = p.grad.data.float()\n",
    "                if grad.is_sparse:\n",
    "                    raise RuntimeError('Ranger optimizer does not support sparse gradients')\n",
    "\n",
    "                p_data_fp32 = p.data.float()\n",
    "\n",
    "                state = self.state[p]  #get state dict for this param\n",
    "\n",
    "                if len(state) == 0:   #if first time to run...init dictionary with our desired entries\n",
    "                    #if self.first_run_check==0:\n",
    "                        #self.first_run_check=1\n",
    "                        #print(\"Initializing slow buffer...should not see this at load from saved model!\")\n",
    "                    state['step'] = 0\n",
    "                    state['exp_avg'] = torch.zeros_like(p_data_fp32)\n",
    "                    state['exp_avg_sq'] = torch.zeros_like(p_data_fp32)\n",
    "\n",
    "                    #look ahead weight storage now in state dict \n",
    "                    state['slow_buffer'] = torch.empty_like(p.data)\n",
    "                    state['slow_buffer'].copy_(p.data)\n",
    "\n",
    "                else:\n",
    "                    state['exp_avg'] = state['exp_avg'].type_as(p_data_fp32)\n",
    "                    state['exp_avg_sq'] = state['exp_avg_sq'].type_as(p_data_fp32)\n",
    "\n",
    "                #begin computations \n",
    "                exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq']\n",
    "                beta1, beta2 = group['betas']\n",
    "\n",
    "                #compute variance mov avg\n",
    "                exp_avg_sq.mul_(beta2).addcmul_(1 - beta2, grad, grad)\n",
    "                #compute mean moving avg\n",
    "                exp_avg.mul_(beta1).add_(1 - beta1, grad)\n",
    "\n",
    "                state['step'] += 1\n",
    "\n",
    "\n",
    "                buffered = self.radam_buffer[int(state['step'] % 10)]\n",
    "                if state['step'] == buffered[0]:\n",
    "                    N_sma, step_size = buffered[1], buffered[2]\n",
    "                else:\n",
    "                    buffered[0] = state['step']\n",
    "                    beta2_t = beta2 ** state['step']\n",
    "                    N_sma_max = 2 / (1 - beta2) - 1\n",
    "                    N_sma = N_sma_max - 2 * state['step'] * beta2_t / (1 - beta2_t)\n",
    "                    buffered[1] = N_sma\n",
    "                    if N_sma > self.N_sma_threshhold:\n",
    "                        step_size = math.sqrt((1 - beta2_t) * (N_sma - 4) / (N_sma_max - 4) * (N_sma - 2) / N_sma * N_sma_max / (N_sma_max - 2)) / (1 - beta1 ** state['step'])\n",
    "                    else:\n",
    "                        step_size = 1.0 / (1 - beta1 ** state['step'])\n",
    "                    buffered[2] = step_size\n",
    "\n",
    "                if group['weight_decay'] != 0:\n",
    "                    p_data_fp32.add_(-group['weight_decay'] * group['lr'], p_data_fp32)\n",
    "\n",
    "                if N_sma > self.N_sma_threshhold:\n",
    "                    denom = exp_avg_sq.sqrt().add_(group['eps'])\n",
    "                    p_data_fp32.addcdiv_(-step_size * group['lr'], exp_avg, denom)\n",
    "                else:\n",
    "                    p_data_fp32.add_(-step_size * group['lr'], exp_avg)\n",
    "\n",
    "                p.data.copy_(p_data_fp32)\n",
    "\n",
    "                #integrated look ahead...\n",
    "                #we do it at the param level instead of group level\n",
    "                if state['step'] % group['k'] == 0:\n",
    "                    slow_p = state['slow_buffer'] #get access to slow param tensor\n",
    "                    slow_p.add_(self.alpha, p.data - slow_p)  #(fast weights - slow weights) * alpha\n",
    "                    p.data.copy_(slow_p)  #copy interpolated weights to RAdam param tensor\n",
    "\n",
    "        return loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_data(size=128, woof=1, bs=64, workers=None, **kwargs):\n",
    "    if woof:\n",
    "        path = URLs.IMAGEWOOF    # if woof \n",
    "    else:\n",
    "        path = URLs.IMAGENETTE\n",
    "    path = untar_data(path)\n",
    "    print('data path  ', path)\n",
    "    n_gpus = num_distrib() or 1\n",
    "    if workers is None: workers = min(8, num_cpus()//n_gpus)\n",
    "    return (ImageList.from_folder(path).split_by_folder(valid='val')\n",
    "            .label_from_folder().transform(([flip_lr(p=0.5)], []), size=size)\n",
    "            .databunch(bs=bs, num_workers=workers)\n",
    "            .presize(size, scale=(0.35,1))\n",
    "            .normalize(imagenet_stats))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_learn(\n",
    "        gpu:Param(\"GPU to run on\", str)=None,\n",
    "        woof: Param(\"Use imagewoof (otherwise imagenette)\", int)=1,\n",
    "        size: Param(\"Size (px: 128,192,224)\", int)=128,\n",
    "        alpha: Param(\"Alpha\", float)=0.99, \n",
    "        mom: Param(\"Momentum\", float)=0.95, #? 0.9\n",
    "        eps: Param(\"epsilon\", float)=1e-6,\n",
    "        bs: Param(\"Batch size\", int)=64,\n",
    "        mixup: Param(\"Mixup\", float)=0.,\n",
    "        opt: Param(\"Optimizer (adam,rms,sgd)\", str)='ranger',\n",
    "        sa: Param(\"Self-attention\", int)=0,\n",
    "        sym: Param(\"Symmetry for self-attention\", int)=0,\n",
    "        model: Param('model as partial', callable) = xresnet50\n",
    "        ):\n",
    " \n",
    "    if   opt=='adam' : opt_func = partial(optim.Adam, betas=(mom,alpha), eps=eps)\n",
    "    elif opt=='ranger'  : opt_func = partial(Ranger,  betas=(mom,alpha), eps=eps)\n",
    "    data = get_data(size, woof, bs)\n",
    "    learn = (Learner(data, model(), wd=1e-2, opt_func=opt_func,\n",
    "             metrics=[accuracy,top_k_accuracy],\n",
    "             bn_wd=False, true_wd=True,\n",
    "             loss_func = LabelSmoothingCrossEntropy(),))\n",
    "    print('Learn path', learn.path)\n",
    "    if mixup: learn = learn.mixup(alpha=mixup)\n",
    "    return learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot(learn):\n",
    "    learn.recorder.plot_losses()\n",
    "    learn.recorder.plot_metrics()\n",
    "    learn.recorder.plot_lr(show_moms=True)\n",
    "Learner.plot = plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ResBlock"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "class NewResBlock(Module):\n",
    "    def __init__(self, expansion, ni, nh, stride=1, \n",
    "                 conv_layer=ConvLayer, act_fn=act_fn, bn_1st=True,\n",
    "                 pool=nn.AvgPool2d(2, ceil_mode=True), sa=False,sym=False, zero_bn=True):\n",
    "        nf,ni = nh*expansion,ni*expansion\n",
    "        self.reduce = noop if stride==1 else pool\n",
    "        layers  = [(f\"conv_0\", conv_layer(ni, nh, 3, stride=stride, act_fn=act_fn, bn_1st=bn_1st)),\n",
    "                   (f\"conv_1\", conv_layer(nh, nf, 3, zero_bn=zero_bn, act=False, bn_1st=bn_1st))\n",
    "        ] if expansion == 1 else [\n",
    "                   (f\"conv_0\",conv_layer(ni, nh, 1, act_fn=act_fn, bn_1st=bn_1st)),\n",
    "                   (f\"conv_1\",conv_layer(nh, nh, 3, stride=1, act_fn=act_fn, bn_1st=bn_1st)), #!!!\n",
    "                   (f\"conv_2\",conv_layer(nh, nf, 1, zero_bn=zero_bn, act=False, bn_1st=bn_1st))\n",
    "        ]\n",
    "        if sa: layers.append(('sa', SimpleSelfAttention(nf,ks=1,sym=sym)))\n",
    "        self.convs = nn.Sequential(OrderedDict(layers))\n",
    "        self.idconv = noop if ni==nf else conv_layer(ni, nf, 1, act=False)\n",
    "        self.merge =act_fn\n",
    "\n",
    "    def forward(self, x): \n",
    "        o = self.reduce(x)\n",
    "        return self.merge(self.convs(o) + self.idconv(o))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr = 0.004\n",
    "epochs = 5\n",
    "moms = (0.95,0.95)\n",
    "start_pct = 0.72\n",
    "size=192\n",
    "bs=64"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Constructor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = xresnet50(c_out=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.block = NewResBlock"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pool = MaxBlurPool2d(3, True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.pool = pool\n",
    "model.stem_pool = pool"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mish activation loaded...\n"
     ]
    }
   ],
   "source": [
    "# model.stem_sizes = [3,32,32,64]\n",
    "model.stem_sizes = [3,32,64,64]\n",
    "\n",
    "model.act_fn= Mish()\n",
    "model.sa = True"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## repr model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sequential(\n",
       "  model xresnet50\n",
       "  (stem): Sequential(\n",
       "    (conv_0): ConvLayer(\n",
       "      (conv): Conv2d(3, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "      (bn): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (act_fn): Mish()\n",
       "    )\n",
       "    (conv_1): ConvLayer(\n",
       "      (conv): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (act_fn): Mish()\n",
       "    )\n",
       "    (conv_2): ConvLayer(\n",
       "      (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "      (bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (act_fn): Mish()\n",
       "    )\n",
       "    (stem_pool): MaxBlurPool2d()\n",
       "  )\n",
       "  (body): Sequential(\n",
       "    (l_0): Sequential(\n",
       "      (bl_0): NewResBlock(\n",
       "        (convs): Sequential(\n",
       "          (conv_0): ConvLayer(\n",
       "            (conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_1): ConvLayer(\n",
       "            (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_2): ConvLayer(\n",
       "            (conv): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          )\n",
       "        )\n",
       "        (idconv): ConvLayer(\n",
       "          (conv): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "        (merge): Mish()\n",
       "      )\n",
       "      (bl_1): NewResBlock(\n",
       "        (convs): Sequential(\n",
       "          (conv_0): ConvLayer(\n",
       "            (conv): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_1): ConvLayer(\n",
       "            (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_2): ConvLayer(\n",
       "            (conv): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          )\n",
       "        )\n",
       "        (merge): Mish()\n",
       "      )\n",
       "      (bl_2): NewResBlock(\n",
       "        (convs): Sequential(\n",
       "          (conv_0): ConvLayer(\n",
       "            (conv): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_1): ConvLayer(\n",
       "            (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_2): ConvLayer(\n",
       "            (conv): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          )\n",
       "          (sa): SimpleSelfAttention(\n",
       "            (conv): Conv1d(256, 256, kernel_size=(1,), stride=(1,), bias=False)\n",
       "          )\n",
       "        )\n",
       "        (merge): Mish()\n",
       "      )\n",
       "    )\n",
       "    (l_1): Sequential(\n",
       "      (bl_0): NewResBlock(\n",
       "        (reduce): MaxBlurPool2d()\n",
       "        (convs): Sequential(\n",
       "          (conv_0): ConvLayer(\n",
       "            (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_1): ConvLayer(\n",
       "            (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_2): ConvLayer(\n",
       "            (conv): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          )\n",
       "        )\n",
       "        (idconv): ConvLayer(\n",
       "          (conv): Conv2d(256, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "        (merge): Mish()\n",
       "      )\n",
       "      (bl_1): NewResBlock(\n",
       "        (convs): Sequential(\n",
       "          (conv_0): ConvLayer(\n",
       "            (conv): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_1): ConvLayer(\n",
       "            (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_2): ConvLayer(\n",
       "            (conv): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          )\n",
       "        )\n",
       "        (merge): Mish()\n",
       "      )\n",
       "      (bl_2): NewResBlock(\n",
       "        (convs): Sequential(\n",
       "          (conv_0): ConvLayer(\n",
       "            (conv): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_1): ConvLayer(\n",
       "            (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_2): ConvLayer(\n",
       "            (conv): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          )\n",
       "        )\n",
       "        (merge): Mish()\n",
       "      )\n",
       "      (bl_3): NewResBlock(\n",
       "        (convs): Sequential(\n",
       "          (conv_0): ConvLayer(\n",
       "            (conv): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_1): ConvLayer(\n",
       "            (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_2): ConvLayer(\n",
       "            (conv): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          )\n",
       "        )\n",
       "        (merge): Mish()\n",
       "      )\n",
       "    )\n",
       "    (l_2): Sequential(\n",
       "      (bl_0): NewResBlock(\n",
       "        (reduce): MaxBlurPool2d()\n",
       "        (convs): Sequential(\n",
       "          (conv_0): ConvLayer(\n",
       "            (conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_1): ConvLayer(\n",
       "            (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_2): ConvLayer(\n",
       "            (conv): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          )\n",
       "        )\n",
       "        (idconv): ConvLayer(\n",
       "          (conv): Conv2d(512, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "        (merge): Mish()\n",
       "      )\n",
       "      (bl_1): NewResBlock(\n",
       "        (convs): Sequential(\n",
       "          (conv_0): ConvLayer(\n",
       "            (conv): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_1): ConvLayer(\n",
       "            (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_2): ConvLayer(\n",
       "            (conv): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          )\n",
       "        )\n",
       "        (merge): Mish()\n",
       "      )\n",
       "      (bl_2): NewResBlock(\n",
       "        (convs): Sequential(\n",
       "          (conv_0): ConvLayer(\n",
       "            (conv): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_1): ConvLayer(\n",
       "            (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_2): ConvLayer(\n",
       "            (conv): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          )\n",
       "        )\n",
       "        (merge): Mish()\n",
       "      )\n",
       "      (bl_3): NewResBlock(\n",
       "        (convs): Sequential(\n",
       "          (conv_0): ConvLayer(\n",
       "            (conv): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_1): ConvLayer(\n",
       "            (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_2): ConvLayer(\n",
       "            (conv): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          )\n",
       "        )\n",
       "        (merge): Mish()\n",
       "      )\n",
       "      (bl_4): NewResBlock(\n",
       "        (convs): Sequential(\n",
       "          (conv_0): ConvLayer(\n",
       "            (conv): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_1): ConvLayer(\n",
       "            (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_2): ConvLayer(\n",
       "            (conv): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          )\n",
       "        )\n",
       "        (merge): Mish()\n",
       "      )\n",
       "      (bl_5): NewResBlock(\n",
       "        (convs): Sequential(\n",
       "          (conv_0): ConvLayer(\n",
       "            (conv): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_1): ConvLayer(\n",
       "            (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_2): ConvLayer(\n",
       "            (conv): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          )\n",
       "        )\n",
       "        (merge): Mish()\n",
       "      )\n",
       "    )\n",
       "    (l_3): Sequential(\n",
       "      (bl_0): NewResBlock(\n",
       "        (reduce): MaxBlurPool2d()\n",
       "        (convs): Sequential(\n",
       "          (conv_0): ConvLayer(\n",
       "            (conv): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_1): ConvLayer(\n",
       "            (conv): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_2): ConvLayer(\n",
       "            (conv): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          )\n",
       "        )\n",
       "        (idconv): ConvLayer(\n",
       "          (conv): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "        (merge): Mish()\n",
       "      )\n",
       "      (bl_1): NewResBlock(\n",
       "        (convs): Sequential(\n",
       "          (conv_0): ConvLayer(\n",
       "            (conv): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_1): ConvLayer(\n",
       "            (conv): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_2): ConvLayer(\n",
       "            (conv): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          )\n",
       "        )\n",
       "        (merge): Mish()\n",
       "      )\n",
       "      (bl_2): NewResBlock(\n",
       "        (convs): Sequential(\n",
       "          (conv_0): ConvLayer(\n",
       "            (conv): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_1): ConvLayer(\n",
       "            (conv): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "            (act_fn): Mish()\n",
       "          )\n",
       "          (conv_2): ConvLayer(\n",
       "            (conv): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "            (bn): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          )\n",
       "        )\n",
       "        (merge): Mish()\n",
       "      )\n",
       "    )\n",
       "  )\n",
       "  (head): Sequential(\n",
       "    (pool): AdaptiveAvgPool2d(output_size=1)\n",
       "    (flat): Flatten()\n",
       "    (fc): Linear(in_features=2048, out_features=10, bias=True)\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": null,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sequential(\n",
       "  (conv_0): ConvLayer(\n",
       "    (conv): Conv2d(3, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "    (bn): BatchNorm2d(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    (act_fn): Mish()\n",
       "  )\n",
       "  (conv_1): ConvLayer(\n",
       "    (conv): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "    (bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    (act_fn): Mish()\n",
       "  )\n",
       "  (conv_2): ConvLayer(\n",
       "    (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "    (bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "    (act_fn): Mish()\n",
       "  )\n",
       "  (stem_pool): MaxBlurPool2d()\n",
       ")"
      ]
     },
     "execution_count": null,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.stem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sequential(\n",
       "  (l_0): Sequential(\n",
       "    (bl_0): NewResBlock(\n",
       "      (convs): Sequential(\n",
       "        (conv_0): ConvLayer(\n",
       "          (conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_1): ConvLayer(\n",
       "          (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_2): ConvLayer(\n",
       "          (conv): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (idconv): ConvLayer(\n",
       "        (conv): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "      (merge): Mish()\n",
       "    )\n",
       "    (bl_1): NewResBlock(\n",
       "      (convs): Sequential(\n",
       "        (conv_0): ConvLayer(\n",
       "          (conv): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_1): ConvLayer(\n",
       "          (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_2): ConvLayer(\n",
       "          (conv): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (merge): Mish()\n",
       "    )\n",
       "    (bl_2): NewResBlock(\n",
       "      (convs): Sequential(\n",
       "        (conv_0): ConvLayer(\n",
       "          (conv): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_1): ConvLayer(\n",
       "          (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_2): ConvLayer(\n",
       "          (conv): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "        (sa): SimpleSelfAttention(\n",
       "          (conv): Conv1d(256, 256, kernel_size=(1,), stride=(1,), bias=False)\n",
       "        )\n",
       "      )\n",
       "      (merge): Mish()\n",
       "    )\n",
       "  )\n",
       "  (l_1): Sequential(\n",
       "    (bl_0): NewResBlock(\n",
       "      (reduce): MaxBlurPool2d()\n",
       "      (convs): Sequential(\n",
       "        (conv_0): ConvLayer(\n",
       "          (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_1): ConvLayer(\n",
       "          (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_2): ConvLayer(\n",
       "          (conv): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (idconv): ConvLayer(\n",
       "        (conv): Conv2d(256, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "      (merge): Mish()\n",
       "    )\n",
       "    (bl_1): NewResBlock(\n",
       "      (convs): Sequential(\n",
       "        (conv_0): ConvLayer(\n",
       "          (conv): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_1): ConvLayer(\n",
       "          (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_2): ConvLayer(\n",
       "          (conv): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (merge): Mish()\n",
       "    )\n",
       "    (bl_2): NewResBlock(\n",
       "      (convs): Sequential(\n",
       "        (conv_0): ConvLayer(\n",
       "          (conv): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_1): ConvLayer(\n",
       "          (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_2): ConvLayer(\n",
       "          (conv): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (merge): Mish()\n",
       "    )\n",
       "    (bl_3): NewResBlock(\n",
       "      (convs): Sequential(\n",
       "        (conv_0): ConvLayer(\n",
       "          (conv): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_1): ConvLayer(\n",
       "          (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_2): ConvLayer(\n",
       "          (conv): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (merge): Mish()\n",
       "    )\n",
       "  )\n",
       "  (l_2): Sequential(\n",
       "    (bl_0): NewResBlock(\n",
       "      (reduce): MaxBlurPool2d()\n",
       "      (convs): Sequential(\n",
       "        (conv_0): ConvLayer(\n",
       "          (conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_1): ConvLayer(\n",
       "          (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_2): ConvLayer(\n",
       "          (conv): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (idconv): ConvLayer(\n",
       "        (conv): Conv2d(512, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "      (merge): Mish()\n",
       "    )\n",
       "    (bl_1): NewResBlock(\n",
       "      (convs): Sequential(\n",
       "        (conv_0): ConvLayer(\n",
       "          (conv): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_1): ConvLayer(\n",
       "          (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_2): ConvLayer(\n",
       "          (conv): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (merge): Mish()\n",
       "    )\n",
       "    (bl_2): NewResBlock(\n",
       "      (convs): Sequential(\n",
       "        (conv_0): ConvLayer(\n",
       "          (conv): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_1): ConvLayer(\n",
       "          (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_2): ConvLayer(\n",
       "          (conv): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (merge): Mish()\n",
       "    )\n",
       "    (bl_3): NewResBlock(\n",
       "      (convs): Sequential(\n",
       "        (conv_0): ConvLayer(\n",
       "          (conv): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_1): ConvLayer(\n",
       "          (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_2): ConvLayer(\n",
       "          (conv): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (merge): Mish()\n",
       "    )\n",
       "    (bl_4): NewResBlock(\n",
       "      (convs): Sequential(\n",
       "        (conv_0): ConvLayer(\n",
       "          (conv): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_1): ConvLayer(\n",
       "          (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_2): ConvLayer(\n",
       "          (conv): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (merge): Mish()\n",
       "    )\n",
       "    (bl_5): NewResBlock(\n",
       "      (convs): Sequential(\n",
       "        (conv_0): ConvLayer(\n",
       "          (conv): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_1): ConvLayer(\n",
       "          (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_2): ConvLayer(\n",
       "          (conv): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (merge): Mish()\n",
       "    )\n",
       "  )\n",
       "  (l_3): Sequential(\n",
       "    (bl_0): NewResBlock(\n",
       "      (reduce): MaxBlurPool2d()\n",
       "      (convs): Sequential(\n",
       "        (conv_0): ConvLayer(\n",
       "          (conv): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_1): ConvLayer(\n",
       "          (conv): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_2): ConvLayer(\n",
       "          (conv): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (idconv): ConvLayer(\n",
       "        (conv): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (bn): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "      (merge): Mish()\n",
       "    )\n",
       "    (bl_1): NewResBlock(\n",
       "      (convs): Sequential(\n",
       "        (conv_0): ConvLayer(\n",
       "          (conv): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_1): ConvLayer(\n",
       "          (conv): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_2): ConvLayer(\n",
       "          (conv): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (merge): Mish()\n",
       "    )\n",
       "    (bl_2): NewResBlock(\n",
       "      (convs): Sequential(\n",
       "        (conv_0): ConvLayer(\n",
       "          (conv): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_1): ConvLayer(\n",
       "          (conv): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "          (act_fn): Mish()\n",
       "        )\n",
       "        (conv_2): ConvLayer(\n",
       "          (conv): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "          (bn): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "        )\n",
       "      )\n",
       "      (merge): Mish()\n",
       "    )\n",
       "  )\n",
       ")"
      ]
     },
     "execution_count": null,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.body"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sequential(\n",
       "  (pool): AdaptiveAvgPool2d(output_size=1)\n",
       "  (flat): Flatten()\n",
       "  (fc): Linear(in_features=2048, out_features=10, bias=True)\n",
       ")"
      ]
     },
     "execution_count": null,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.head"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lr find"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading https://s3.amazonaws.com/fast-ai-imageclas/imagewoof2\n"
     ]
    },
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data path   /root/.fastai/data/imagewoof2\n",
      "Learn path /root/.fastai/data/imagewoof2\n"
     ]
    }
   ],
   "source": [
    "learn = get_learn(model=model,size=size,bs=bs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "        <style>\n",
       "            /* Turns off some styling */\n",
       "            progress {\n",
       "                /* gets rid of default border in Firefox and Opera. */\n",
       "                border: none;\n",
       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "                background-size: auto;\n",
       "            }\n",
       "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "                background: #F44336;\n",
       "            }\n",
       "        </style>\n",
       "      <progress value='0' class='' max='1', style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      0.00% [0/1 00:00<00:00]\n",
       "    </div>\n",
       "    \n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>top_k_accuracy</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table><p>\n",
       "\n",
       "    <div>\n",
       "        <style>\n",
       "            /* Turns off some styling */\n",
       "            progress {\n",
       "                /* gets rid of default border in Firefox and Opera. */\n",
       "                border: none;\n",
       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "                background-size: auto;\n",
       "            }\n",
       "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
       "                background: #F44336;\n",
       "            }\n",
       "        </style>\n",
       "      <progress value='92' class='' max='141', style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      65.25% [92/141 01:11<00:38 9.4246]\n",
       "    </div>\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "set state called\n",
      "LR Finder is complete, type {learner_name}.recorder.plot() to see the graph.\n"
     ]
    }
   ],
   "source": [
    "learn.lr_find()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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xTbcOAB8Pmvr/YmZd4YV6VNOqh5m9B9jt7hvDDvQopv1ZmNlfmNku4APAjSHG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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.recorder.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# epochs 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data path   /root/.fastai/data/imagewoof2\n",
      "Learn path /root/.fastai/data/imagewoof2\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
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       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>top_k_accuracy</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
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       "      <td>0</td>\n",
       "      <td>1.914692</td>\n",
       "      <td>1.928615</td>\n",
       "      <td>0.394757</td>\n",
       "      <td>0.836854</td>\n",
       "      <td>02:25</td>\n",
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       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1.667191</td>\n",
       "      <td>1.575827</td>\n",
       "      <td>0.528124</td>\n",
       "      <td>0.921863</td>\n",
       "      <td>02:27</td>\n",
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       "      <td>0.585645</td>\n",
       "      <td>0.933571</td>\n",
       "      <td>02:26</td>\n",
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       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1.364018</td>\n",
       "      <td>1.275540</td>\n",
       "      <td>0.692034</td>\n",
       "      <td>0.954441</td>\n",
       "      <td>02:27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1.159506</td>\n",
       "      <td>1.107725</td>\n",
       "      <td>0.765335</td>\n",
       "      <td>0.976075</td>\n",
       "      <td>02:26</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
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       "<IPython.core.display.HTML object>"
      ]
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     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn = get_learn(model=model,size=size,bs=bs)\n",
    "learn.fit_fc(epochs, lr, moms,start_pct)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data path   /root/.fastai/data/imagewoof2\n",
      "Learn path /root/.fastai/data/imagewoof2\n"
     ]
    },
    {
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       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>top_k_accuracy</th>\n",
       "      <th>time</th>\n",
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       "      <td>0.924663</td>\n",
       "      <td>02:25</td>\n",
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       "      <td>0.909646</td>\n",
       "      <td>02:26</td>\n",
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       "      <td>1.265066</td>\n",
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       "      <td>0.963858</td>\n",
       "      <td>02:26</td>\n",
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       "  </tbody>\n",
       "</table>"
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      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn = get_learn(model=model,size=size,bs=bs)\n",
    "learn.fit_fc(epochs, lr, moms,start_pct)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data path   /root/.fastai/data/imagewoof2\n",
      "Learn path /root/.fastai/data/imagewoof2\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
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       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>top_k_accuracy</th>\n",
       "      <th>time</th>\n",
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       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1.500411</td>\n",
       "      <td>1.491567</td>\n",
       "      <td>0.566047</td>\n",
       "      <td>0.933062</td>\n",
       "      <td>02:26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1.353897</td>\n",
       "      <td>1.247568</td>\n",
       "      <td>0.687198</td>\n",
       "      <td>0.961059</td>\n",
       "      <td>02:26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1.140543</td>\n",
       "      <td>1.098308</td>\n",
       "      <td>0.763808</td>\n",
       "      <td>0.972258</td>\n",
       "      <td>02:26</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn = get_learn(model=model,size=size,bs=bs)\n",
    "learn.fit_fc(epochs, lr, moms,start_pct)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data path   /root/.fastai/data/imagewoof2\n",
      "Learn path /root/.fastai/data/imagewoof2\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>top_k_accuracy</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1.935801</td>\n",
       "      <td>1.972722</td>\n",
       "      <td>0.360652</td>\n",
       "      <td>0.815729</td>\n",
       "      <td>02:26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1.678253</td>\n",
       "      <td>1.525489</td>\n",
       "      <td>0.547468</td>\n",
       "      <td>0.935862</td>\n",
       "      <td>02:26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1.506853</td>\n",
       "      <td>1.717256</td>\n",
       "      <td>0.463477</td>\n",
       "      <td>0.906592</td>\n",
       "      <td>02:25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1.384003</td>\n",
       "      <td>1.271321</td>\n",
       "      <td>0.681344</td>\n",
       "      <td>0.954950</td>\n",
       "      <td>02:26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1.167252</td>\n",
       "      <td>1.106536</td>\n",
       "      <td>0.763044</td>\n",
       "      <td>0.972003</td>\n",
       "      <td>02:26</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn = get_learn(model=model,size=size,bs=bs)\n",
    "learn.fit_fc(epochs, lr, moms,start_pct)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data path   /root/.fastai/data/imagewoof2\n",
      "Learn path /root/.fastai/data/imagewoof2\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>top_k_accuracy</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1.927778</td>\n",
       "      <td>1.909708</td>\n",
       "      <td>0.400611</td>\n",
       "      <td>0.867396</td>\n",
       "      <td>02:26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1.665046</td>\n",
       "      <td>1.542189</td>\n",
       "      <td>0.550267</td>\n",
       "      <td>0.927717</td>\n",
       "      <td>02:25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1.491803</td>\n",
       "      <td>1.609370</td>\n",
       "      <td>0.537796</td>\n",
       "      <td>0.907865</td>\n",
       "      <td>02:26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1.349082</td>\n",
       "      <td>1.258505</td>\n",
       "      <td>0.682871</td>\n",
       "      <td>0.954696</td>\n",
       "      <td>02:26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1.131855</td>\n",
       "      <td>1.103346</td>\n",
       "      <td>0.764571</td>\n",
       "      <td>0.970476</td>\n",
       "      <td>02:26</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn = get_learn(model=model,size=size,bs=bs)\n",
    "learn.fit_fc(epochs, lr, moms,start_pct)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# e5 results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "acc = np.array([0.765335, 0.770934, 0.763808, 0.763044, 0.764571])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.7655384, 0.0028037849132913255)"
      ]
     },
     "execution_count": null,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acc.mean(), acc.std()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# epochs 20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "epochs = 20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data path   /root/.fastai/data/imagewoof2\n",
      "Learn path /root/.fastai/data/imagewoof2\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>top_k_accuracy</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1.951780</td>\n",
       "      <td>1.987291</td>\n",
       "      <td>0.379995</td>\n",
       "      <td>0.865360</td>\n",
       "      <td>02:26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1.687755</td>\n",
       "      <td>1.546163</td>\n",
       "      <td>0.547722</td>\n",
       "      <td>0.923390</td>\n",
       "      <td>02:26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1.509401</td>\n",
       "      <td>1.653405</td>\n",
       "      <td>0.493764</td>\n",
       "      <td>0.904301</td>\n",
       "      <td>02:25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1.368347</td>\n",
       "      <td>1.285907</td>\n",
       "      <td>0.671163</td>\n",
       "      <td>0.958259</td>\n",
       "      <td>02:26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1.281009</td>\n",
       "      <td>1.367182</td>\n",
       "      <td>0.637821</td>\n",
       "      <td>0.954187</td>\n",
       "      <td>02:26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>1.188061</td>\n",
       "      <td>1.211238</td>\n",
       "      <td>0.719012</td>\n",
       "      <td>0.962331</td>\n",
       "      <td>02:25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>1.137729</td>\n",
       "      <td>1.372902</td>\n",
       "      <td>0.648766</td>\n",
       "      <td>0.953423</td>\n",
       "      <td>02:26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>1.055464</td>\n",
       "      <td>1.095949</td>\n",
       "      <td>0.768898</td>\n",
       "      <td>0.970476</td>\n",
       "      <td>02:25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>1.022705</td>\n",
       "      <td>1.217260</td>\n",
       "      <td>0.733520</td>\n",
       "      <td>0.962077</td>\n",
       "      <td>02:26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>0.994688</td>\n",
       "      <td>1.015727</td>\n",
       "      <td>0.800713</td>\n",
       "      <td>0.976075</td>\n",
       "      <td>02:26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>0.942580</td>\n",
       "      <td>1.087502</td>\n",
       "      <td>0.767116</td>\n",
       "      <td>0.970730</td>\n",
       "      <td>02:26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>0.915394</td>\n",
       "      <td>0.988866</td>\n",
       "      <td>0.809875</td>\n",
       "      <td>0.980148</td>\n",
       "      <td>02:25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>0.889199</td>\n",
       "      <td>1.113151</td>\n",
       "      <td>0.752609</td>\n",
       "      <td>0.966913</td>\n",
       "      <td>02:25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>0.863562</td>\n",
       "      <td>0.947819</td>\n",
       "      <td>0.828710</td>\n",
       "      <td>0.981675</td>\n",
       "      <td>02:25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>0.833907</td>\n",
       "      <td>0.984263</td>\n",
       "      <td>0.815984</td>\n",
       "      <td>0.981166</td>\n",
       "      <td>02:26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>0.793831</td>\n",
       "      <td>0.897212</td>\n",
       "      <td>0.846271</td>\n",
       "      <td>0.983456</td>\n",
       "      <td>02:25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>0.740043</td>\n",
       "      <td>0.884069</td>\n",
       "      <td>0.855688</td>\n",
       "      <td>0.983202</td>\n",
       "      <td>02:26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>0.686534</td>\n",
       "      <td>0.860831</td>\n",
       "      <td>0.863833</td>\n",
       "      <td>0.982438</td>\n",
       "      <td>02:26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>0.648914</td>\n",
       "      <td>0.838373</td>\n",
       "      <td>0.874523</td>\n",
       "      <td>0.985747</td>\n",
       "      <td>02:26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>0.619747</td>\n",
       "      <td>0.831208</td>\n",
       "      <td>0.874014</td>\n",
       "      <td>0.986256</td>\n",
       "      <td>02:26</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn = get_learn(model=model,size=size,bs=bs)\n",
    "learn.fit_fc(epochs, lr, moms,start_pct)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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vco6/fa6TgDxRdXpUpWyDB9Y47SLGmCIr9nTfphLy84eYm2HbN/DqRfDNMxDV\nG7/7lnH7cx9yXS+nOunamEgGtqsPOA3Z7Z76ksv++R2rfz3E4fTMc89bqwncMgs0F969BlL3FD22\ngzvg/VFQs45zV5JfcgD3LuIvcGw/rHiz6O9ljDkvu4Ooig7tcpJD3dZwxd+gZb8zdh/PyiE40Llb\nSEo9Tve/f3vOKUbERPLSqGjk7OqfxFXwztVQq6lzB1AjonAxpR2AtwdBxkG482sntsL433WQuBIe\njIfgsMIdY4w5xSd3ECIyWUSSRGRdPvtvEpF4EflZRH4Ukeg8+3a629eIiH3jl7bw5vDwBrh70TnJ\nATiVHADqhwWzc8JQNoy/4owys1Yn8sCMNWRk5px5cGRXGP0+HPzFuRvITDt/PJlpTtnURBjzQeGT\nAzhtERmHYJmXx2MYUwV5s4ppKjC4gP07gEtVtRPwLDDprP39VbVLfpnNlFDNuk51UyHVCApg54Sh\nZySLz9bu4ZUFW88t3PJSGDnZ+cv+g1sg20OV1Ek52fDxHbBntdNttlmPol1H4xhnfMSPr0J6MaYO\nMcbky2sJQlUXAfn+j1XVH1X1kPtyGdDEW7GY0lUjKIC5D/QBYPaaPeR46urU7mq4+mX45Vtn/qTc\nnHPLqMIXDzs9qq58AdpdVbyA+j8Jmcfgh4nFO94Y41F5aaS+E8g70kqBr0RkpYiMK+hAERknInEi\nEpecnOzVIM1p7RuH8eL10SQezqDVE3M5dsLDqOautzhTY6z/BOb98dzJ/Ra9AKvegT5/gIvuKn4w\n9dtB51HOyO+j+4p/HmPMGXyeIESkP06CeCzP5t6q2hUYAtwnIn3zO15VJ6lqrKrG1qtXz8vRmryu\njm586nnHp+cT9fgXZOWcNYlerwedx4q3YOHfT29f9a4z6V70GKc3Ukn1exxys2DxiyU/lzEG8HGC\nEJHOwFvAcFVNObldVRPdn0nALKC7byI0BQkK8GPLc0OoUzPo1LbWT85jXeIRjmflsC3pKEfSs8jq\n/zTE3AKLnodlrztTeXz2ILS6zPNAuOKIaOl0342bAod/Lfn5zmazx5oqyKvdXEUkCvhcVTt62NcM\nWADcqqo/5tleE/BT1aPu86+B8ar65fnez7q5+o6qMuI/P7Jm92GP+18f05khGx+HTZ9DQDDUvbDg\ngXDFcSTRmVyw8/Uw/LXSO++OxfDx7RB7B/R/ovTOa0w54KturtOBpUAbEUkQkTtF5B4Rucct8hRQ\nB/jPWd1ZGwBLRGQt8BPwRWGSg/EtEeHT+3pxQf0Qj/vvnR7PxNqPOXcNYZHnHwhXHLUi4aI7ndll\nD2wrnXOumuYM/DtxDL5/HnGJx1oAABzbSURBVLZ/XzrnNaYCsIFyplRl5+SSnat8sGI3PVpG0LZh\nGIu3JnPL2ycXJ1KWPNqXJnW8NKjtWDL8OxraDHa62hZXbo4zyvzHl52kNvw/zgDAzDS494fCDwA0\nppyzqTZMmQnw9yM40J+xl0TRtqGTBPq0rsf/7jw5vkEY/p/leO0Pk5B60PMeWDcT9nkco3l+J445\n4zd+fNnpXXXjRxDWyFmwKC0ZPnvA1sU2VYIlCFMmereuy/InBgCQkpbJmDeXee/NLrkfqtUq3pKo\nRxJhymDYMg+GPA9DXwT/AGdf4y4w4C+w8TObRdZUCZYgTJlpEBbMNw87PZaXbT/IpS8s5FBaJrm5\nSmZ2KfYSqh4Ove53VqJLKEKVY+JKePMyOLgTbvwQetx9bpmL74cWfeHLx0uvncOYcsraIEyZ27Qv\nlcETz10Lu0l4dSbdEku90GoEBfgR5O9X8BTjBTlxzGmLaNgRbp19/vLrP3VGfIfUd+aDatA+/7Kp\ne5wV92o3dyYWDAjKv6wx5VxBbRCWIIxPZOfkMvmHHfx97qZ8y4QGBxD/9KBzZ4wtrKWvwfwnYOzn\n0KKP5zKqztKmC56Dpj3ghvecdozz2fgZfHAz9HoILv9r8eIzphywBGHKrV0paXy5bh89WtZh9KSl\nHM86t6qpT+u6TLujO28u3s7mfcf4+7UdqRZQiDuLrOPOuIjaTZ1FjM5ONNknnJXr4j+ATqOcQXuB\nwYUPfs4DTlvE2DlOtZMxFZAlCFNhpJ3IRoFjx7O56a1l/JJ87nTh13aN5KVRXQp3wrjJ8PnvnZ5I\nFw7K80YHYMZNsHsZ9P8z9H2k6CO6M9Pgv30hM926vpoKy7q5mgqjZrUAQqoF0LBWMN/+oR9fPtSH\n6oFn3i18siqRXhMWFK5hO+YWCI+CBc+eni4jaZPTGL13DYycApc+WrzpPoJq5un6+mDl6/pa2a7H\nFJklCFOutW0YxsZnBzP19ov4+J6L2fzcYLpHRZB4OIO3lmzn2InsgsdU+AdCvz/BvnjYOMdZavXt\nyyErA26bCx2vLVmAjWOcRYs2zoHV75bsXOXJxs/h+Raw/L++jsT4kFUxmQonN1dp+cTcM7a9dWss\nA9s3yOeAHKfXUXqK86jfHsbMcNomSicgeHe406X27sVQ94LSOa8vqDoTKs5/wrlDykyDG9511vcw\nlZJVMZlKxc9PeOvWM3+f75oWR9TjX/D8l5tIO3ttCj9/Z1GhtGRoPQju+LL0koMTEIz4L/gHwSd3\nFbyCXlEd3Q/Jm0vvfAXJyXbW7Zj/JychPPQzNImFmXcVbTyJqTTsDsJUSKrK3iPHaVQrmHWJqVz9\n6pJT+xqGBdOvTT1q1QjktkuiqFU9kBpBAZC00ZlFtghLrRbJhjnw4S3Q+/cw8JmSnSt1r7NCXtwU\nyDkBsXfCoGedv+q94cQxmHmns7rfJffDwPFO4ks7AG8NcPbf9bUzrbqpVKwXk6n0Fmzaz/ebk9mX\nepz56/efu/8Pl9KynueZZkvVnPudxZCK2/U1dQ8smQgrp0JuNnS50Zn1dtnrTmP7iDegWc/SjTl1\nL7w/Cvavc5Z+PXt1vwPb4O2BUKOOMzDQemtVKpYgTJUy46dfeWr2ei5rW58v159egvTPQ9vRJLwG\nX23Yx+iLmtG9hRe+6Irb9TVvYtAcZ6W9Pn+AiBbO/p1L4NN74UgCXPKAsy5FQLWSx7t/Pbw3Co4f\nhuunQuvLPZfbtRSmDYfIbnDLrKKNFzHlmiUIU2VlZOYw/vMNTP/p3FXmZt/Xi+imtUv/TRNXOT2l\n2g6F698puAtt6h5Y8i9Y+Y6TGLrc6CSG8Khzy5446jQer5oG9TvAtf+Fhp2KH+e2b+HDsVAtxJl7\nqlHngsuvmwkf3wEdr4Nr33KqoEyFZ43UpsqqHuTP/13biXfu6E6t6oEA3NKzOQDDX/uBD+N2l/6b\nRnZ1ur5umA2r/+e5TOoemPuoM19U3GSIHg33r3JGc3tKDuBUNQ17xfkyT0uGSf2dNbhzsj2XL8jK\nqfDe9RDeHO769vzJAZzEMPAZJ1EseLbo72kqHLuDMFXShyt288eZ8QB0ax7OP67rxMxVidx+SRT1\nw4LJyMzhH19uokl4da6Obky1AD9q1yjCpHy5uTBtmHM3cc9iqNPK2X7qjmEqaC50ucm9Y2hetAtI\nS4EvHoYNn0KTi5xeVCff43xxLRjvxHDBQKdaqSgr+6k6I9NXToGrJkLs7UWL25Q7VsVkjAdfxO/l\nvvdXFbr8wkf60aJuEXoRHUl0xl9EtHC+iH98FVa94ySGmJuh98NFTwx5qTp/zX/xMORkweXjnQbm\n/Kq0so477RjrP3HW1x7ywum1LooiJxumj4ZfFsCNH+TfbmEqBJ9VMYnIZBFJEhGPS3uJ42UR2SYi\n8SLSNc++sSKy1X2M9Wacpmoa2rkRPz8ziGtjIqke6M+AtvULLH/d6z8ye00iObkF/1Glqs7o7lqR\nMOxl2LPaqUpaOdVpY7h/FVz975IlB3ASQaeR8Ntl0OximPsIvDvCSUxnS0tx7mjWf+IkkqEvFS85\ngHPc9VOgQQf46DbYG1+iyzDll1fvIESkL3AMmKaqHT3svxK4H7gS6AH8W1V7iEgEEAfEAgqsBLqp\n6qGC3s/uIExxqSoiwonsHFbsOMQlrerg53f6L/HfTIvj6w1O99mhnRsx8YYuBPqf+/fV8u0p3DDJ\nWS3vr8M6cMNFTQn+8UU4lgS9HoDazbx1AU61z/wnwS/Q6a7aeZSTRFJ+gfdGOtVbI/4LHa4pnfdM\n3QtvDXQa1+/6Bmo1KZ3zmjLl0yomEYkCPs8nQfwX+E5Vp7uvNwP9Tj5U9W5P5fJjCcJ4y57DGfxt\n7kYSD2WwZvdhAC5v34CQagHsTElj9EVNeXrOeo/TlbdtGMrzIzvTuYkXekyd7eB2mHWvM0ttu6sh\n+kaY/VsQf2d6kaYXle777V8PkwdDraZwxzwIrlW65zdeV54TxOfABFVd4r7+FngMJ0EEq+pz7va/\nABmq+k8P5xgHjANo1qxZt127dnnnQoxxRf/1K45kZOW7/+N7LqZGUABXvnzmqnmf39+bjpFl8AWa\nmwM/vuKsyZ2TCXVaw00fnR5TUdp+WejcoUT1cd7HP7D45zpxzElu1WqVfjIzHlXqBJGX3UGYsvJF\n/F4270vl14PpdGlam1b1Q9h5II1+berTNKIG4FRbfbQygYWbkpi3zhmw99RV7bmxRzOCA7003Ude\n+9fDzx87U2d4e/Tz6vecO5WYm2HYq4WfPj0z3UkIOxY7gwH3rHJGkAN0GAFX/B3CGnsvblNggihm\nK1WpSQTyzprWxN2WiJMk8m7/rsyiMuY8hnZuxNDOjc7Y1qf1mUuVigijYpsyKrYpX63fx7h3VzL+\n8w38ejCdZ4Z1AE63fXhFgw7OoyzE3ASHd8H3/4DaUc4aG55kZUDCCjchLHYmAczNAr8AaNzVGSUe\n1dvZvvhF2Po19HscetxTsjsTUyy+voMYCvyO043UL6tqd7eReiVwslfTKpxG6oMFvZfdQZjy7Mdf\nDvDU7PVsSzp2zr77L7uAhEMZzFrt9EAa0LY+b94ae0ZDebmnCrPugfgZMGISRN/gLOuaEOckgx2L\nneSQcwLEDxp1cdYKj+rrzC9V7ay5sg7ugHmPwdb5zhTtV/4Tonr55toqMZ9VMYnIdJw7gbrAfuBp\nIBBAVd8Q50+nV4HBQDpwu6rGucfeATzhnupvqjrlfO9nCcKUd7tS0rj0he8KXf6nJwYQ4O9HeI1A\n791plKbsTPjftfDrMudLPyEOsjMAcUZrR/VxJjFs1rNwDdqqsHkuzHscjvwKnUc7s9qGFNwl2RSe\nDZQzphzZcziDd5ftYlyfloTXDGLN7sPcOXUFIjD19u4E+Auj3lhK6vHTU2jc3LMZzw7vWDGSRMZh\np9E6K8NNCH2g+SVQPbz458xMh8X/hB9ehsAazlQmsXcUfyyHOcUShDEV0DNz1jP1x51nbPvLVe25\no1cUG/cepX3jMN8E5ksHtjoDArd/50xUOPRf1tuphCxBGFMBqSqJhzNoVKs6z36+4ZxkAfDOHd1p\n1yiUeiHVEBGycnLZlnSMNxdt56edB3n+us5cckHdsg/em1Rh/SxnZtujeyHmFhj4V6hZx9eRVUiW\nIIypBFbuOsR1r/9Y5OOGRTfm5TExXojIx04cdXpNLXvdmXBwwNPQdaxNQ15EliCMqSSOZGSRlHqc\n1g1Cmf7Tr/zpk5+59MJ6fL8l+Yxy7RuF8ew1HU8llPAagbx7Z4+yGahX1pI2whd/gF0/OAsaXT4e\n6rZxxn54a3nZSsQShDFVwInsHAQhKOD0X9CH0jKJefbrfI95e2wsd75z+v/Mn4a05fXvf+FwehZj\nujfjr8M6nHG+cksV4j+Er/4MaUnONvGDmvWgZn2n19PJR82znzdwGtCr6J2HJQhjqrDM7FyGv/YD\nx7Ny2HEgrcjH/+uGaEbEVJCJ+I4fcaYhP5bkPvY7iyudfJ2W5Ew/cja/ACeZhDRw1tWo1xbqXuj8\njGgJAUVYC6SCsQRhjAHg6PEsMjJzCPD3Y8XOgyzclMSAdg249MJ6fLtxP28u3k7vC+pyyQV1WbQl\nmf989wsA4/q2JKRaAJe0qkNslDNtx+H0TOat28fV0Y0JqXa6u6lXR4eXlKqTRE4mi7yJ49h+Z4ba\nlK1wOM8SteLvJIl6bZxH3ZM/W0NQEdYHKacsQRhjimXj3lSG/PvMSQffuaM79723imMnCl7qtMwm\nJ/SGzDSnS+2BLZC8CZI3O89TfnGmNz+pVrM8iePC0z+9PfdVKbIEYYwptl9T0hn/+QZyVVn96yEO\npZ+eybZl3ZpsL6DaqntUBNPu7F42kxOWhexMZ0r15E1u8tjsPFK2Qvbx0+Vq1HESRd3W7k/3ee3m\n5a7h3BKEMaZU7D6YTp/nF3JtTCQvXB+Nf565og6lZRLgLxw9nk1WTi4j31hK8tETAPzw+GVE1q7u\n8Zypx7OoHujPnsMZRNauToCHhZjKvdwcZ7LCA9ucxHFgy+k7kPQDp8v5B0GdC85NHHVanzsXVRmx\nBGGM8Ylb3l7O4q2nvyAfvaIN9/W/AICsnFxaPznP43Ef3XMxF0VVnGqaAqUfPJ0s8iaOQzuc9clP\nqhYGQSFOoggKcdo3qoXm2VYTgkI97w+uBQ3PmQ+1UCxBGGN8ZuPeVK56ZckZa3lf1bkRCzYlkZ55\nuj6/faMwNuxNPfW6oLuOSiH7hDNj7cnEkZYMmcecRZNO/UyDzKOnt+WtxsqrZn14dGuxwrAEYYzx\nKVXlh20p3Pz28jO239TDmYQQODW1+Zrdh7nmtR8AZxr0PwxqU7bBlmc52U6iyJtIMo85VVwXDCjW\nKS1BGGPKjdlrElm56xC392pBi7qeu4l+vyWZsZN/AqBzk1pMvb07ETXzH4tQrrvWlnOWIIwxFU56\nZja/fW8V320+PY1Io1rB1A2pRpPw6rw8JobsHKXdU18C0DSiOpnZuTw08ELGdG/mq7ArHEsQxpgK\n66v1+3h7yQ6W7yhwQckzTLn9Ivq3sUWFCsMShDGmwjt2IptAfyE3Fx6YsZoFm5LIyVV6tIhg+m96\nsjf1ODNXJvDS11toVCuYD8ZdTLM6NXwddrnnyyVHBwP/BvyBt1R1wln7/wX0d1/WAOqram13Xw7w\ns7vvV1Uddr73swRhjPk54QhXv7oEgN8PvJD7+reqmGMryohPEoSI+ANbgMuBBGAFMEZVN+RT/n4g\nRlXvcF8fU9UijRyxBGGMAfhwxW7+ODP+1OsRMZFMuK4T1QLK1yjm8qCgBOHNtNod2Kaq21U1E5gB\nDC+g/BhguhfjMcZUEaMuasrm5wbTs6Uz2G7W6kTa/PlL7p++muNZOec52pzkzRW/I4HdeV4nAD08\nFRSR5kALYEGezcEiEgdkAxNU9dN8jh0HjANo1sx6LhhjHNUC/Jkx7mKSjh5n5c5D/HFmPJ+t3cNn\na/fQun4I/7qhC/uOHGdb8jE6R9Zi9e7DJBzK4IEBF9CoViUeoFcE3kwQRTEa+Fg17zSJNFfVRBFp\nCSwQkZ9V9ZezD1TVScAkcKqYyiZcY0xFUT80mCGdGtG/bX0+WpnAXz5dx9akY1z1yhKP5ZdvT2HW\nfb2oVT2wjCMtf7xZxZQINM3zuom7zZPRnFW9pKqJ7s/twHdAJVxU1xhTVoID/bmlZ3N2ThjKV7/v\nS0i1AIZ0bMiDA1oDcEWHBrz/mx7sPpTO3e/GcexENhmZVbs6ypuN1AE4jdQDcBLDCuBGVV1/Vrm2\nwJdAC3WDEZFwIF1VT4hIXWApMDy/Bu6TrJHaGFNS7/y4k6fnnP6a6hgZxkujunBhg1AfRuU9BTVS\ne62KSVWzReR3wHycbq6TVXW9iIwH4lR1jlt0NDBDz8xU7YD/ikguzl3OhPMlB2OMKQ1jL4kC4Ok5\n6/ETWJeYyqB/LWJgu/oM6xLJ4A4NK8Y63aXABsoZY0wBVv96iJvfWk6aW90UFhxAjaAAGoRVIzQ4\nkN9f3ppAfz/CawTRNKLiDcyzkdTGGFNCB9My+Tx+D0/NXp9vmQcHtOahga0r1MSBliCMMaYUZefk\nsnjbAdYlHCFu1yHW70nlwDFn9bz+berx5ND2XFDfNyvEFZUlCGOM8bLcXOX1739h4jdbyMlVWtcP\n5cmh7ejTum65vqOwBGGMMWVkf+pxHv04nkVbks/YfnuvKPpeWI/wGkGkHDtB/dBgourWINDfj+BA\n300BYgnCGGPK2K6UNBZuSmLuun38dJ6pymsE+XN9tybc1adlmTd0W4IwxhgfW7v7MB+t3E2T8Bqk\nZ+aQfPQ4yUczqR7k3D18Eb+HXIUOjcO4r/8F9L2wHnsOZ3h9/IUlCGOMKed2Hkjjpa+38M3G/aTn\nGcHdqFYwzSJqMLxLJM3r1GBnShrdoyIICvCjTkg1gvz9SjQuwxKEMcZUEDm5yhc/7+WbDfuJTzhM\nRM0gVv16ON/yNYP8ubRNPf5zU7divZ9PRlIbY4wpOn8/YVh0Y4ZFNz61LSsnlw17Uvlqwz4ua9uA\nZdtTOHYimyMZWRxJz/Jau4UlCGOMKecC/f2Iblqb6Ka1AejWPLxM3rdqTChijDGmyCxBGGOM8cgS\nhDHGGI8sQRhjjPHIEoQxxhiPLEEYY4zxyBKEMcYYjyxBGGOM8ahSTbUhIsnArmIeXhc4UIrhlBd2\nXRWLXVfFUhmuq7mq1vO0o1IliJIQkbj85iOpyOy6Kha7roqlsl7XSVbFZIwxxiNLEMYYYzyyBHHa\nJF8H4CV2XRWLXVfFUlmvC7A2CGOMMfmwOwhjjDEeWYIwxhjjUZVPECIyWEQ2i8g2EXnc1/EUlYjs\nFJGfRWSNiMS52yJE5GsR2er+DHe3i4i87F5rvIh09W30p4nIZBFJEpF1ebYV+TpEZKxbfquIjPXF\nteSVz3U9IyKJ7me2RkSuzLPvT+51bRaRK/JsL1e/pyLSVEQWisgGEVkvIg+62yv0Z1bAdVX4z6xY\nVLXKPgB/4BegJRAErAXa+zquIl7DTqDuWdueBx53nz8O/MN9fiUwDxCgJ7Dc1/Hnibkv0BVYV9zr\nACKA7e7PcPd5eDm8rmeARzyUbe/+DlYDWri/m/7l8fcUaAR0dZ+HAlvc+Cv0Z1bAdVX4z6w4j6p+\nB9Ed2Kaq21U1E5gBDPdxTKVhOPCO+/wd4Jo826epYxlQW0Qa+SLAs6nqIuDgWZuLeh1XAF+r6kFV\nPQR8DQz2fvT5y+e68jMcmKGqJ1R1B7AN53e03P2equpeVV3lPj8KbAQiqeCfWQHXlZ8K85kVR1VP\nEJHA7jyvEyj4l6E8UuArEVkpIuPcbQ1Uda/7fB/QwH1e0a63qNdRka7vd25Vy+ST1TBU0OsSkSgg\nBlhOJfrMzrouqESfWWFV9QRRGfRW1a7AEOA+Eembd6c698EVvi9zZbkO1+tAK6ALsBd40bfhFJ+I\nhAAzgYdUNTXvvor8mXm4rkrzmRVFVU8QiUDTPK+buNsqDFVNdH8mAbNwbm33n6w6cn8mucUr2vUW\n9ToqxPWp6n5VzVHVXOBNnM8MKth1iUggzpfoe6r6ibu5wn9mnq6rsnxmRVXVE8QKoLWItBCRIGA0\nMMfHMRWaiNQUkdCTz4FBwDqcazjZG2QsMNt9Pge41e1R0hM4kqc6oDwq6nXMBwaJSLhbBTDI3Vau\nnNXuMwLnMwPnukaLSDURaQG0Bn6iHP6eiogAbwMbVfWlPLsq9GeW33VVhs+sWHzdSu7rB07vii04\nPQ6e9HU8RYy9JU7viLXA+pPxA3WAb4GtwDdAhLtdgNfca/0ZiPX1NeS5luk4t+5ZOPW1dxbnOoA7\ncBoKtwG3l9PreteNOx7nS6NRnvJPute1GRhSXn9Pgd441UfxwBr3cWVF/8wKuK4K/5kV52FTbRhj\njPGoqlcxGWOMyYclCGOMMR5ZgjDGGOORJQhjjDEeWYIwxhjjkSUIU6GISI47m+ZaEVklIpecp3xt\nEfltIc77nYhU2sXni0NEporISF/HYXzHEoSpaDJUtYuqRgN/Av7vPOVrA+dNEL4iIgG+jsGY/FiC\nMBVZGHAInLlzRORb967iZxE5OXPmBKCVe9fxglv2MbfMWhGZkOd814vITyKyRUT6uGX9ReQFEVnh\nTtR2t7u9kYgscs+77mT5vMRZq+N5971+EpEL3O1TReQNEVkOPC/OGgqfuudfJiKd81zTFPf4eBG5\nzt0+SESWutf6kTtvECIyQZx1DOJF5J/utuvd+NaKyKLzXJOIyKvirGHwDVC/ND8sU/HYXy+moqku\nImuAYJy5+y9ztx8HRqhqqojUBZaJyBycNQk6qmoXABEZgjPtcg9VTReRiDznDlDV7uIsBvM0MBBn\n5PMRVb1IRKoBP4jIV8C1wHxV/ZuI+AM18on3iKp2EpFbgYnAVe72JsAlqpojIq8Aq1X1GhG5DJiG\nMyncX04e78Ye7l7bn4GBqpomIo8BD4vIazhTQLRVVRWR2u77PAVcoaqJebbld00xQBucNQ4aABuA\nyYX6VEylZAnCVDQZeb7sLwamiUhHnKkc/i7ObLa5OFMrN/Bw/EBgiqqmA6hq3rUaTk44txKIcp8P\nAjrnqYuvhTPfzgpgsjgTu32qqmvyiXd6np//yrP9I1XNcZ/3Bq5z41kgInVEJMyNdfTJA1T1kIhc\nhfMF/oMzbRBBwFLgCE6SfFtEPgc+dw/7AZgqIh/mub78rqkvMN2Na4+ILMjnmkwVYQnCVFiqutT9\ni7oezrw39YBuqpolIjtx7jKK4oT7M4fT/zcEuF9Vz5lAzk1GQ3G+gF9S1WmewszneVoRYzv1tjgL\n7IzxEE93YAAwEvgdcJmq3iMiPdw4V4pIt/yuSfIso2kMWBuEqcBEpC3O0o4pOH8FJ7nJoT/Q3C12\nFGfpyJO+Bm4XkRruOfJWMXkyH7jXvVNARC4UZxbd5sB+VX0TeAtnWVFPbsjzc2k+ZRYDN7nn7wcc\nUGcNgq+B+/JcbziwDOiVpz2jphtTCFBLVecCvwei3f2tVHW5qj4FJONMQe3xmoBFwA1uG0UjoP95\n/m1MJWd3EKaiOdkGAc5fwmPdevz3gM9E5GcgDtgEoKopIvKDiKwD5qnqoyLSBYgTkUxgLvBEAe/3\nFk510ypx6nSScZbR7Ac8KiJZwDHg1nyODxeReJy7k3P+6nc9g1NdFQ+kc3q67OeA19zYc4C/quon\nInIbMN1tPwCnTeIoMFtEgt1/l4fdfS+ISGt327c4M//G53NNs3DadDYAv5J/QjNVhM3maoyXuNVc\nsap6wNexGFMcVsVkjDHGI7uDMMYY45HdQRhjjPHIEoQxxhiPLEEYY4zxyBKEMcYYjyxBGGOM8ej/\nAfmWVdvPbi6DAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.recorder.plot_losses()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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nvJjkbAZ3DaK1r/ZHKKWajjOam0Kqdibbh6ye8fc0OFNJua0/QqfiUEo1NWck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\nMLOyyftFfz0wX9KvAQHvx9OMllyhaH3eW6aUOxQ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IYHW6fBKwOCIWAUTESxHRWuJ4zcws\no9RJYl/g+cz7urQt60rgo5LqSM4iPpO2vxEISfMkPSbpyyWO1czM2qiEwvW5wC0RMQk4DbhdUhXJ\npbC3AR9Jf54l6cS2O0u6UNJCSQsbGxt7Mm4zsz6v1M9J1AOTM+8npW1ZnwBOAYiI+WlxehzJWcfD\nEbEWQNJc4AjgvuzOEXETcFO6TaOkv5WgH5ViHLC23EGUmPvYN7iPvcuUYitKnSQWANMkTSVJDucA\nH26zzXPAicAtkqYDg4FGYB7wZUlDga3AccB3O/plETG+e8OvLJIWRsSscsdRSu5j3+A+9h0lTRIR\nsV3SxSRf+NXAzRGxTNJVwMKImAN8AfixpM+RFLEviIgA1ku6gSTRBDA3Iu4uZbxmZvZaSr6PrTfo\nD3+5uI99g/vYd1RC4dryu6ncAfQA97FvcB/7CJ9JmJlZUT6TMDOzopwkKoykZ9Nxqp6QtDBtGyPp\nXkkr05+j03ZJ+n46LtZiSUeUN/r2SbpZ0hpJSzNtXe6TpPPT7VdKOr8cfWlPkf5dKak+/RyfaDMm\n2aVp/5ZLOjnT3uE4Z+UkaXI6xlpNOpbaZ9P2vvQ5Futjn/osuywi/KqgF/AsMK5N23eAS9LlS4B/\nSZdPA+4BBBwN/Lnc8Rfp09tJnnFZuqt9AsYAT6c/R6fLo8vdtw76dyXwxXa2nQEsAvYApgJPkdz5\nV50uHwAMSreZUe6+ZeKeCByRLg8HVqR96UufY7E+9qnPsqsvn0n0DmcCt6bLtwLvzbTfFolHgVGS\nJpYjwI5ExMPAujbNXe3TycC9EbEuItYD95I+hFluRfpXzJnAHRGxJSKeAVaRjHGWZ5yzsomIhoh4\nLF1uAWpJhtjpS59jsT4W0ys/y65ykqg8AfxR0l8lXZi2TYiIhnT5BWBCupxnbKxK1dU+9ca+Xpxe\narm5cBmGPtA/SfsDhwN/po9+jm36CH30s8zDSaLyvC0ijgBOBS6S9PbsykjOc/vULWl9sU/Aj4AD\ngcOABuD68obTPSQNA/4T+KeIaM6u6yufYzt97JOfZV5OEhUmIurTn2uA35Kcur5YuIyU/lyTbp5n\nbKxK1dU+9aq+RsSLEdEaETuAH5N8jtCL+ydpIMmX5y8i4jdpc5/6HNvrY1/8LLvCSaKCSNpT0vDC\nMsmcGkuBOUDhLpDzgf9Kl+cA56V3khwNNGVO/StdV/s0DzhJ0uj0dP+ktK0itakNnUXyOULSv3Mk\n7aFkTLNpwF/IjHMmaRDJOGdzejLmjkgS8FOgNiJuyKzqM59jsT72tc+yy8pdOfdr54vkbohF6WsZ\ncFnaPpZk9NuVwJ+AMWm7SGb+ewpYAswqdx+K9OtXJKfp20iuz35iV/oE/D1JcXAV8PFy96uT/t2e\nxr+Y5AtiYmb7y9L+LQdOzbSfRnJHzVOFz75SXiTD9UfanyfS12l97HMs1sc+9Vl29eUnrs3MrChf\nbjIzs6KcJMzMrCgnCTMzK8pJwszMinKSMDOzopwkrNeR1JqOxrlI0mOS3trJ9qMkfTrHcR+U1Odn\nGusKSbdIen+547DycZKw3ujliDgsIt4MXApc08n2o4BOk0S5SCrpXPNmu8NJwnq7EcB6SMbckXRf\nenaxRFJh5M1vAwemZx/Xptt+Jd1mkaRvZ473AUl/kbRC0rHpttWSrpW0IB3k7ZNp+0RJD6fHXVrY\nPkvJ/CDfSX/XXyS9IW2/RdK/Sfoz8B0l8zL8Lj3+o5JmZvr0s3T/xZLel7afJGl+2tdfp+MNIenb\nSuZDWCzpurTtA2l8iyQ93EmfJOmHSuZC+BOwV3d+WNb7+C8Y642GSHoCGEwyB8AJafsrwFkR0Sxp\nHPCopDkk8xy8KSIOA5B0KsnQzUdFxGZJYzLHHhARs5VMLHMF8E6SJ6ibIuLvJO0BPCLpj8DZwLyI\nuFpSNTC0SLxNEXGopPOA7wGnp+2TgLdGRKukHwCPR8R7JZ0A3EYyoNzXCvunsY9O+/ZV4J0RsUnS\nV4DPS7qRZNiIgyMiJI1Kf8/lwMkRUZ9pK9anw4GDSOZKmADUADfn+lSsT3KSsN7o5cwX/luA2yS9\niWQoiG8pGTl3B8nwzBPa2f+dwM8iYjNARGTngigMXPdXYP90+SRgZuba/EiScXoWADcrGRTudxHx\nRJF4f5X5+d1M+68jojVdfhvwvjSe+yWNlTQijfWcwg4RsV7S6SRf4o8kww0xCJgPNJEkyp9K+j3w\n+3S3R4BbJN2Z6V+xPr0d+FUa12pJ9xfpk/UTThLWq0XE/PQv6/Ek4+WMB46MiG2SniU52+iKLenP\nVnb+/xDwmYh43UB0aUJ6N8mX8A0RcVt7YRZZ3tTF2F79tSQT95zbTjyzgROB9wMXAydExKckHZXG\n+VdJRxbrkzJTc5qBaxLWy0k6mGS6yJdI/hpekyaIdwBT0s1aSKajLLgX+Likoekxspeb2jMP+Mf0\njAFJb1QyYu8U4MWI+DHwE5IpTNvzoczP+UW2+b/AR9LjHw+sjWQug3uBizL9HQ08ChyTqW/smcY0\nDBgZEXOBzwFvTtcfGBF/jojLgUaSYazb7RPwMPChtGYxEXhHJ/821sf5TMJ6o0JNApK/iM9Pr+v/\nAvhvSUuAhcCTABHxkqRHJC0F7omIL0k6DFgoaSswF/jnDn7fT0guPT2m5PpOI8k0nccDX5K0DdgI\nnFdk/9GSFpOcpbzur//UlSSXrhYDm9k5/PY3gRvT2FuBr0fEbyRdAPwqrSdAUqNoAf5L0uD03+Xz\n6bprJU1L2+4jGWV4cZE+/ZakxlMDPEfxpGb9hEeBNSuh9JLXrIhYW+5YzHaFLzeZmVlRPpMwM7Oi\nfCZhZmZFOUmYmVlRThJmZlaUk4SZmRXlJGFmZkU5SZiZWVH/H6TY9jrziW0uAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.recorder.plot_metrics()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data path   /root/.fastai/data/imagewoof2\n",
      "Learn path /root/.fastai/data/imagewoof2\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>top_k_accuracy</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1.928996</td>\n",
       "      <td>2.130869</td>\n",
       "      <td>0.330618</td>\n",
       "      <td>0.799186</td>\n",
       "      <td>02:26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1.654571</td>\n",
       "      <td>1.510041</td>\n",
       "      <td>0.563248</td>\n",
       "      <td>0.931535</td>\n",
       "      <td>02:25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1.492199</td>\n",
       "      <td>1.637613</td>\n",
       "      <td>0.545686</td>\n",
       "      <td>0.927208</td>\n",
       "      <td>02:26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1.380559</td>\n",
       "      <td>1.242368</td>\n",
       "      <td>0.691779</td>\n",
       "      <td>0.964368</td>\n",
       "      <td>02:25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1.272474</td>\n",
       "      <td>1.339306</td>\n",
       "      <td>0.662510</td>\n",
       "      <td>0.939934</td>\n",
       "      <td>02:25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>1.215663</td>\n",
       "      <td>1.128977</td>\n",
       "      <td>0.745737</td>\n",
       "      <td>0.970221</td>\n",
       "      <td>02:25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>1.130999</td>\n",
       "      <td>1.244253</td>\n",
       "      <td>0.711377</td>\n",
       "      <td>0.966913</td>\n",
       "      <td>02:26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>1.071108</td>\n",
       "      <td>1.131425</td>\n",
       "      <td>0.748282</td>\n",
       "      <td>0.974039</td>\n",
       "      <td>02:25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>1.029524</td>\n",
       "      <td>1.087102</td>\n",
       "      <td>0.765844</td>\n",
       "      <td>0.972767</td>\n",
       "      <td>02:25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>0.988084</td>\n",
       "      <td>1.033931</td>\n",
       "      <td>0.785696</td>\n",
       "      <td>0.972258</td>\n",
       "      <td>02:26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>0.950812</td>\n",
       "      <td>1.098684</td>\n",
       "      <td>0.757954</td>\n",
       "      <td>0.967422</td>\n",
       "      <td>02:26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>0.925120</td>\n",
       "      <td>0.993044</td>\n",
       "      <td>0.811657</td>\n",
       "      <td>0.976839</td>\n",
       "      <td>02:26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>0.891522</td>\n",
       "      <td>1.065330</td>\n",
       "      <td>0.783660</td>\n",
       "      <td>0.969458</td>\n",
       "      <td>02:26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>0.864949</td>\n",
       "      <td>0.929542</td>\n",
       "      <td>0.830491</td>\n",
       "      <td>0.986256</td>\n",
       "      <td>02:26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>0.843885</td>\n",
       "      <td>1.030024</td>\n",
       "      <td>0.783660</td>\n",
       "      <td>0.976584</td>\n",
       "      <td>02:26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>0.797855</td>\n",
       "      <td>0.914339</td>\n",
       "      <td>0.839654</td>\n",
       "      <td>0.983456</td>\n",
       "      <td>02:26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>0.739738</td>\n",
       "      <td>0.925721</td>\n",
       "      <td>0.838636</td>\n",
       "      <td>0.981675</td>\n",
       "      <td>02:26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>0.682849</td>\n",
       "      <td>0.847647</td>\n",
       "      <td>0.870960</td>\n",
       "      <td>0.985747</td>\n",
       "      <td>02:26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>0.639613</td>\n",
       "      <td>0.843618</td>\n",
       "      <td>0.872232</td>\n",
       "      <td>0.985238</td>\n",
       "      <td>02:26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>0.618057</td>\n",
       "      <td>0.835195</td>\n",
       "      <td>0.874014</td>\n",
       "      <td>0.984729</td>\n",
       "      <td>02:26</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn = get_learn(model=model,size=size,bs=bs)\n",
    "learn.fit_fc(epochs, lr, moms,start_pct)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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LbGxs0QR+Fld2qAfA9BVnTL0R57VDJFk1kzGm9AtkLyYB3gZWq+rzuez2CXCj15upG5Ci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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.recorder.plot_losses()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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HtWZHCbVuZWxK3JELHyI6KpxL0lK4JC2FgpJKIsNC/VBDY4xpOn/e5/EL4Chg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\nYfmRDjVeR0n7AwcDD1Gn32O3OkIdfo9FOXjUjg9GxHuB44D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      "text/plain": [
       "<Figure size 432x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.recorder.plot_metrics()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data path   /root/.fastai/data/imagewoof2\n",
      "Learn path /root/.fastai/data/imagewoof2\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>top_k_accuracy</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1.944520</td>\n",
       "      <td>1.851911</td>\n",
       "      <td>0.435225</td>\n",
       "      <td>0.888267</td>\n",
       "      <td>02:25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1.682538</td>\n",
       "      <td>1.556184</td>\n",
       "      <td>0.542123</td>\n",
       "      <td>0.924154</td>\n",
       "      <td>02:25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1.514457</td>\n",
       "      <td>1.487957</td>\n",
       "      <td>0.580300</td>\n",
       "      <td>0.931026</td>\n",
       "      <td>02:26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1.368677</td>\n",
       "      <td>1.225495</td>\n",
       "      <td>0.700433</td>\n",
       "      <td>0.961059</td>\n",
       "      <td>02:26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1.269385</td>\n",
       "      <td>1.338023</td>\n",
       "      <td>0.648002</td>\n",
       "      <td>0.950115</td>\n",
       "      <td>02:26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>1.204454</td>\n",
       "      <td>1.132066</td>\n",
       "      <td>0.750827</td>\n",
       "      <td>0.969458</td>\n",
       "      <td>02:27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>1.129814</td>\n",
       "      <td>1.500308</td>\n",
       "      <td>0.603970</td>\n",
       "      <td>0.944006</td>\n",
       "      <td>02:26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>1.067564</td>\n",
       "      <td>1.032067</td>\n",
       "      <td>0.792568</td>\n",
       "      <td>0.973785</td>\n",
       "      <td>02:26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>1.015332</td>\n",
       "      <td>1.056225</td>\n",
       "      <td>0.783915</td>\n",
       "      <td>0.972258</td>\n",
       "      <td>02:26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>0.972254</td>\n",
       "      <td>0.969717</td>\n",
       "      <td>0.820565</td>\n",
       "      <td>0.976330</td>\n",
       "      <td>02:27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>0.929694</td>\n",
       "      <td>1.027453</td>\n",
       "      <td>0.788750</td>\n",
       "      <td>0.972258</td>\n",
       "      <td>02:26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>0.917512</td>\n",
       "      <td>0.957854</td>\n",
       "      <td>0.819801</td>\n",
       "      <td>0.981166</td>\n",
       "      <td>02:26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>0.879602</td>\n",
       "      <td>1.025144</td>\n",
       "      <td>0.793586</td>\n",
       "      <td>0.975312</td>\n",
       "      <td>02:26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>0.856776</td>\n",
       "      <td>0.959949</td>\n",
       "      <td>0.819547</td>\n",
       "      <td>0.976330</td>\n",
       "      <td>02:26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>0.824178</td>\n",
       "      <td>0.957232</td>\n",
       "      <td>0.824128</td>\n",
       "      <td>0.979130</td>\n",
       "      <td>02:26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>0.787227</td>\n",
       "      <td>0.910532</td>\n",
       "      <td>0.841690</td>\n",
       "      <td>0.981929</td>\n",
       "      <td>02:26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>0.739938</td>\n",
       "      <td>0.957617</td>\n",
       "      <td>0.820565</td>\n",
       "      <td>0.980402</td>\n",
       "      <td>02:26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>0.666963</td>\n",
       "      <td>0.866259</td>\n",
       "      <td>0.862306</td>\n",
       "      <td>0.983456</td>\n",
       "      <td>02:26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>0.636770</td>\n",
       "      <td>0.849832</td>\n",
       "      <td>0.869941</td>\n",
       "      <td>0.982184</td>\n",
       "      <td>02:26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>0.623335</td>\n",
       "      <td>0.841852</td>\n",
       "      <td>0.873505</td>\n",
       "      <td>0.982947</td>\n",
       "      <td>02:26</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn = get_learn(model=model,size=size,bs=bs)\n",
    "learn.fit_fc(epochs, lr, moms,start_pct)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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S2R6ICC1rVWLLyCtoVyeaYyezaPz4RD74+zD48jYWn4ij6/5n2B0df3qn4Apw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\nI0sQxhhjPPp/9FYVDo/KnT0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.recorder.plot_losses()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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MMdXoHdd99smeE6OahmZT0wnz02IpqW1h57EaDlc1aX+EUgOMO5KETURSTrwQ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\nTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.recorder.plot_metrics()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data path   /root/.fastai/data/imagewoof2\n",
      "Learn path /root/.fastai/data/imagewoof2\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>top_k_accuracy</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1.914761</td>\n",
       "      <td>2.141501</td>\n",
       "      <td>0.367778</td>\n",
       "      <td>0.844999</td>\n",
       "      <td>02:26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1.670501</td>\n",
       "      <td>1.512637</td>\n",
       "      <td>0.555612</td>\n",
       "      <td>0.929753</td>\n",
       "      <td>02:25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1.507263</td>\n",
       "      <td>1.680600</td>\n",
       "      <td>0.527361</td>\n",
       "      <td>0.881649</td>\n",
       "      <td>02:26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1.372341</td>\n",
       "      <td>1.361309</td>\n",
       "      <td>0.652838</td>\n",
       "      <td>0.947824</td>\n",
       "      <td>02:25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1.293781</td>\n",
       "      <td>1.302903</td>\n",
       "      <td>0.668363</td>\n",
       "      <td>0.950369</td>\n",
       "      <td>02:26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>1.190584</td>\n",
       "      <td>1.169150</td>\n",
       "      <td>0.733265</td>\n",
       "      <td>0.966913</td>\n",
       "      <td>02:25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>1.117490</td>\n",
       "      <td>1.165836</td>\n",
       "      <td>0.734793</td>\n",
       "      <td>0.961822</td>\n",
       "      <td>02:25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>1.075199</td>\n",
       "      <td>1.056878</td>\n",
       "      <td>0.780606</td>\n",
       "      <td>0.973530</td>\n",
       "      <td>02:26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>1.026502</td>\n",
       "      <td>1.201843</td>\n",
       "      <td>0.724357</td>\n",
       "      <td>0.965895</td>\n",
       "      <td>02:25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>0.994273</td>\n",
       "      <td>1.008623</td>\n",
       "      <td>0.808857</td>\n",
       "      <td>0.978621</td>\n",
       "      <td>02:26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>0.942573</td>\n",
       "      <td>1.042944</td>\n",
       "      <td>0.784678</td>\n",
       "      <td>0.975566</td>\n",
       "      <td>02:26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>0.911720</td>\n",
       "      <td>0.942805</td>\n",
       "      <td>0.832782</td>\n",
       "      <td>0.983711</td>\n",
       "      <td>02:26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>0.896990</td>\n",
       "      <td>0.993073</td>\n",
       "      <td>0.805548</td>\n",
       "      <td>0.978875</td>\n",
       "      <td>02:26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>0.862459</td>\n",
       "      <td>0.920181</td>\n",
       "      <td>0.839145</td>\n",
       "      <td>0.981929</td>\n",
       "      <td>02:26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>0.828251</td>\n",
       "      <td>1.058278</td>\n",
       "      <td>0.775261</td>\n",
       "      <td>0.978621</td>\n",
       "      <td>02:26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>0.800606</td>\n",
       "      <td>0.907429</td>\n",
       "      <td>0.848053</td>\n",
       "      <td>0.983456</td>\n",
       "      <td>02:26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>0.752835</td>\n",
       "      <td>0.905793</td>\n",
       "      <td>0.849580</td>\n",
       "      <td>0.984474</td>\n",
       "      <td>02:26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>0.686375</td>\n",
       "      <td>0.847837</td>\n",
       "      <td>0.870960</td>\n",
       "      <td>0.983965</td>\n",
       "      <td>02:26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>0.637446</td>\n",
       "      <td>0.829877</td>\n",
       "      <td>0.877577</td>\n",
       "      <td>0.984729</td>\n",
       "      <td>02:26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>0.620570</td>\n",
       "      <td>0.826353</td>\n",
       "      <td>0.877322</td>\n",
       "      <td>0.985492</td>\n",
       "      <td>02:26</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn = get_learn(model=model,size=size,bs=bs)\n",
    "learn.fit_fc(epochs, lr, moms,start_pct)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ko6fOFoY3B//a2g+hlPJomiDK6Mz8TNf8d9nZQi8viOysU24opTyaJogyio4IJLZBMEdO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\nOaUJQimllFP/D5TlVR13MWH9AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.recorder.plot_losses()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading https://s3.amazonaws.com/fast-ai-imageclas/imagewoof2\n"
     ]
    },
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data path   /root/.fastai/data/imagewoof2\n",
      "Learn path /root/.fastai/data/imagewoof2\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: left;\">\n",
       "      <th>epoch</th>\n",
       "      <th>train_loss</th>\n",
       "      <th>valid_loss</th>\n",
       "      <th>accuracy</th>\n",
       "      <th>top_k_accuracy</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1.939362</td>\n",
       "      <td>1.915429</td>\n",
       "      <td>0.413337</td>\n",
       "      <td>0.875541</td>\n",
       "      <td>02:21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1.707571</td>\n",
       "      <td>1.595436</td>\n",
       "      <td>0.534742</td>\n",
       "      <td>0.928990</td>\n",
       "      <td>02:25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>1.528139</td>\n",
       "      <td>1.589575</td>\n",
       "      <td>0.504709</td>\n",
       "      <td>0.932807</td>\n",
       "      <td>02:25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>1.407868</td>\n",
       "      <td>1.285636</td>\n",
       "      <td>0.677526</td>\n",
       "      <td>0.953678</td>\n",
       "      <td>02:24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>1.292768</td>\n",
       "      <td>1.764858</td>\n",
       "      <td>0.535505</td>\n",
       "      <td>0.916009</td>\n",
       "      <td>02:25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>1.211878</td>\n",
       "      <td>1.143014</td>\n",
       "      <td>0.747773</td>\n",
       "      <td>0.969203</td>\n",
       "      <td>02:24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>1.165059</td>\n",
       "      <td>1.231655</td>\n",
       "      <td>0.704759</td>\n",
       "      <td>0.954696</td>\n",
       "      <td>02:24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>1.091453</td>\n",
       "      <td>1.068479</td>\n",
       "      <td>0.773479</td>\n",
       "      <td>0.975566</td>\n",
       "      <td>02:25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>1.030882</td>\n",
       "      <td>1.133523</td>\n",
       "      <td>0.753118</td>\n",
       "      <td>0.974548</td>\n",
       "      <td>02:24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>1.005048</td>\n",
       "      <td>1.043115</td>\n",
       "      <td>0.789259</td>\n",
       "      <td>0.976330</td>\n",
       "      <td>02:24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>0.946171</td>\n",
       "      <td>1.144482</td>\n",
       "      <td>0.747009</td>\n",
       "      <td>0.967167</td>\n",
       "      <td>02:25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>0.941341</td>\n",
       "      <td>0.973369</td>\n",
       "      <td>0.816493</td>\n",
       "      <td>0.978621</td>\n",
       "      <td>02:25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>0.903893</td>\n",
       "      <td>1.043992</td>\n",
       "      <td>0.784933</td>\n",
       "      <td>0.975821</td>\n",
       "      <td>02:25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>0.877880</td>\n",
       "      <td>0.961328</td>\n",
       "      <td>0.827437</td>\n",
       "      <td>0.980402</td>\n",
       "      <td>02:25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>14</td>\n",
       "      <td>0.842926</td>\n",
       "      <td>1.020794</td>\n",
       "      <td>0.801731</td>\n",
       "      <td>0.971749</td>\n",
       "      <td>02:23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>15</td>\n",
       "      <td>0.811568</td>\n",
       "      <td>0.910248</td>\n",
       "      <td>0.836600</td>\n",
       "      <td>0.981420</td>\n",
       "      <td>02:24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>16</td>\n",
       "      <td>0.760785</td>\n",
       "      <td>0.925119</td>\n",
       "      <td>0.844490</td>\n",
       "      <td>0.979893</td>\n",
       "      <td>02:23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>17</td>\n",
       "      <td>0.691999</td>\n",
       "      <td>0.877122</td>\n",
       "      <td>0.856961</td>\n",
       "      <td>0.985492</td>\n",
       "      <td>02:24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>18</td>\n",
       "      <td>0.640048</td>\n",
       "      <td>0.851362</td>\n",
       "      <td>0.866887</td>\n",
       "      <td>0.984220</td>\n",
       "      <td>02:24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>19</td>\n",
       "      <td>0.619693</td>\n",
       "      <td>0.843908</td>\n",
       "      <td>0.873250</td>\n",
       "      <td>0.985238</td>\n",
       "      <td>02:24</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn = get_learn(model=model,size=size,bs=bs)\n",
    "learn.fit_fc(epochs, lr, moms,start_pct)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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1gYB62lGtlBvTBFFV9HnUGim9cda5smM7kZP7uGTwKJ67uh2vDusIQNQri+j7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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.recorder.plot_losses()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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B49jrL7R3midFRUV18C66v+R9JYwdEEqAT8emoCRGBzEgzL/NTU5puWWEBXgz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\n5pwHiVGgP/IAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "learn.recorder.plot_metrics()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# e20 results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "acc = np.array([0.874014, 0.874014, 0.873505, 0.877322, 0.873250])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.8744209999999999, 0.001480364549697165)"
      ]
     },
     "execution_count": null,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acc.mean(), acc.std()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
